Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition

Abstract

The ability to control external devices through thought is increasingly becoming a reality. Human beings can use the electrical signals of their brain to interact or change the surrounding environment and more. The development of this technology called brain-computer interface (BCI) will increasingly allow people with motor disabilities to communicate or use assistive devices to walk, manipulate objects and communicate. Using data from the PhysioNet database, this study implemented a pattern classification system for use in a BCI on 109 healthy volunteers during real movement activities and motor imagery recorded by 64-channels electroencephalography (EEG) system. Different classifiers such as Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Trees (TREE) were applied on different combinations of EEG channels. Starting from two channels (C3, C4 and CP3 and CP4) positioned on the contralateral and ipsilateral sensorimotor cortex, the Region of Interest (RoI) centred on C3/Cp3 and C4/Cp4 and, finally, a data-driven automatic channels selection was tested to explore the best channel combination able to increase the classification accuracy. The results showed that the proposed automatic channels selection was able to significantly improve the performance of each classifier achieving 98% of accuracy for classification of real and imagined hand movement (sensitivity = 97%, specificity = 99%, AUC = 0.99) by SVM. While the accuracy of the classification between the imagery of hand and foot movements was 91% (sensitivity = 87%, specificity = 86%, AUC = 0.93) also with SVM. In the proposed approach, the data-driven automatic channels selection outperforms classical a priori channel selection models such as C3/C4, Cp3/Cp4, or RoIs around those channels with the utmost accuracy to help remove the boundaries of human communication and improve the quality of life of people with disabilities.

Brain Dynamics Complexity as a Signature of Cognitive Decline in Parkinson's Disease

“…To enhance the classification process, an optimization approach was employed to select informative features from the 595 pairwise distances and separately run for the FD and fALFF values. The feature selection process followed this procedure, as previously described 37 . The features were first ranked based on their one‐way analysis of variance (ANOVA) F ‐values using the Python function F‐classif from Python Scikit‐learn library.…”

“…The feature selection process followed this procedure, as previously described. 37 The features were first ranked based on their one-way analysis of variance (ANOVA) F-values using the Python function F-classif from Python Scikit-learn library. This initial step is independent of features classification.…”

Survey on the research direction of EEG-based signal processing

“…A detailed table with information on the directions and performance of each paper can be found in the Supplementary material . The following reviewed papers are presented in ascending order of their published date (Aellen et al, 2021 ; Asheri et al, 2021 ; Ashwini and Nagaraj, 2021 ; Awais et al, 2021 ; Cai et al, 2021 ; Dagdevir and Tokmakci, 2021 ; De Venuto and Mezzina, 2021 ; Du et al, 2021 ; Fan et al, 2021 , 2022 ; Ferracuti et al, 2021 ; Gao N. et al, 2021 ; Gao Z. et al, 2021 ; Gaur et al, 2021 ; Lashgari et al, 2021 ; Lian et al, 2021 ; Liu and Jin, 2021 ; Liu and Yang, 2021 ; Liu et al, 2021 ; Qi et al, 2021 ; Rashid et al, 2021 ; Sun et al, 2021 ; Varsehi and Firoozabadi, 2021 ; Vega et al, 2021 ; Vorontsova et al, 2021 ; Wahid and Tafreshi, 2021 ; Wang and Quan, 2021 ; Xu C. et al, 2021 ; Xu F. et al, 2021 ; Yin et al, 2021 ; Zhang K. et al, 2021 ; Zhang Y. et al, 2021 ; Algarni et al, 2022 ; Ali et al, 2022 ; Asadzadeh et al, 2022 ; Ayoobi and Sadeghian, 2022 ; Bagchi and Bathula, 2022 ; Chang et al, 2022 ; Chen J. et al, 2022 ; Chen L. et al, 2022 ; Cui et al, 2022 ; Geng et al, 2022 ; Islam et al, 2022 ; Jia et al, 2022 ; Kim et al, 2022 ; Ko et al, 2022 ; Li and Sun, 2022 ; Li H. et al, 2022 ; Lin et al, 2022 ; Li Q. et al, 2022 ; Lu et al, 2022 ; Ma et al, 2022 ; Mattioli et al, 2022 ;...…”

State-of-the-Art on Brain-Computer Interface Technology

“…On the other non-invasive neural interfaces have been gaining traction in the gaming industry [1 As more types of neurogadgets become available, it is possible that this sector will rience a revolution. It is envisaged that smartphones may be able to record h thoughts in the foreseeable future; research into this area has been ongoing [20,21].…”

“…collected from EEG recordings can also be analyzed to assess cognitive abilities such as attention span or memory recall speed. Figure 3 illustrates that there are four distinct "rhythms" of the human brain, which can be categorized based on their frequency: δ delta (0.1-4 Hz), θ theta (4-7.5 Hz), α alpha (7.5-12 Hz), β beta (12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and γ gamma (over 30 Hz). It is important to note that these rhythms differ in amplitude as well as frequency.…”